{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/nisha/Dropbox/Casualties of War/APSA 2015/Summer 2018/Sent to ISQ/Slightly revised version/Revisions for R&R/Resubmitted to ISQ/Corrected version for R&R/First postaccepted version/life&limbreplication.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 3 Aug 2020, 12:27:36

{com}. do "/Users/nisha/Dropbox/Casualties of War/APSA 2015/Summer 2018/Sent to ISQ/Slightly revised version/Revisions for R&R/Resubmitted to ISQ/Corrected version for R&R/First postaccepted version/YouGovlogits.do"
{txt}
{com}. *this is a do file for regressions for the February 2016 survey on casualties of war*
. logit support t1 t2 t3 t4 t5 t6 t7 closemilitaryties female married employment democrat nonwhite

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-814.19159}  
Iteration 1:{space 3}log likelihood = {res:-779.67635}  
Iteration 2:{space 3}log likelihood = {res:-779.52134}  
Iteration 3:{space 3}log likelihood = {res:-779.52132}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,201
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}     69.34
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-779.52132{txt}{col 49}Pseudo R2{col 67}= {res}    0.0426

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          support{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}t1 {c |}{col 19}{res}{space 2}-.1879343{col 31}{space 2} .2415808{col 42}{space 1}   -0.78{col 51}{space 3}0.437{col 59}{space 4}-.6614239{col 72}{space 3} .2855554
{txt}{space 15}t2 {c |}{col 19}{res}{space 2} .1099664{col 31}{space 2} .2397594{col 42}{space 1}    0.46{col 51}{space 3}0.646{col 59}{space 4}-.3599535{col 72}{space 3} .5798862
{txt}{space 15}t3 {c |}{col 19}{res}{space 2}-.0078664{col 31}{space 2} .2397693{col 42}{space 1}   -0.03{col 51}{space 3}0.974{col 59}{space 4}-.4778057{col 72}{space 3} .4620728
{txt}{space 15}t4 {c |}{col 19}{res}{space 2} .1572785{col 31}{space 2} .2394301{col 42}{space 1}    0.66{col 51}{space 3}0.511{col 59}{space 4} -.311996{col 72}{space 3} .6265529
{txt}{space 15}t5 {c |}{col 19}{res}{space 2}-.0509555{col 31}{space 2} .2413135{col 42}{space 1}   -0.21{col 51}{space 3}0.833{col 59}{space 4}-.5239213{col 72}{space 3} .4220103
{txt}{space 15}t6 {c |}{col 19}{res}{space 2} .2589451{col 31}{space 2} .2393605{col 42}{space 1}    1.08{col 51}{space 3}0.279{col 59}{space 4} -.210193{col 72}{space 3} .7280831
{txt}{space 15}t7 {c |}{col 19}{res}{space 2}-.1605163{col 31}{space 2} .2401565{col 42}{space 1}   -0.67{col 51}{space 3}0.504{col 59}{space 4}-.6312143{col 72}{space 3} .3101818
{txt}closemilitaryties {c |}{col 19}{res}{space 2} .3530771{col 31}{space 2} .1376643{col 42}{space 1}    2.56{col 51}{space 3}0.010{col 59}{space 4} .0832601{col 72}{space 3} .6228941
{txt}{space 11}female {c |}{col 19}{res}{space 2}-.0977449{col 31}{space 2} .1236762{col 42}{space 1}   -0.79{col 51}{space 3}0.429{col 59}{space 4}-.3401458{col 72}{space 3}  .144656
{txt}{space 10}married {c |}{col 19}{res}{space 2} .2182081{col 31}{space 2} .1228296{col 42}{space 1}    1.78{col 51}{space 3}0.076{col 59}{space 4}-.0225335{col 72}{space 3} .4589497
{txt}{space 7}employment {c |}{col 19}{res}{space 2} .0143482{col 31}{space 2} .1222334{col 42}{space 1}    0.12{col 51}{space 3}0.907{col 59}{space 4}-.2252249{col 72}{space 3} .2539213
{txt}{space 9}democrat {c |}{col 19}{res}{space 2}-.8624574{col 31}{space 2} .1330743{col 42}{space 1}   -6.48{col 51}{space 3}0.000{col 59}{space 4}-1.123278{col 72}{space 3}-.6016366
{txt}{space 9}nonwhite {c |}{col 19}{res}{space 2}-.0540356{col 31}{space 2} .1428799{col 42}{space 1}   -0.38{col 51}{space 3}0.705{col 59}{space 4} -.334075{col 72}{space 3} .2260038
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} -.221224{col 31}{space 2}  .215537{col 42}{space 1}   -1.03{col 51}{space 3}0.305{col 59}{space 4}-.6436686{col 72}{space 3} .2012207
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit support t1 t2 t3 t4 t5 t6 t7 female married employment democrat nonwhite if closemilitaryties==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-224.48091}  
Iteration 1:{space 3}log likelihood = {res: -206.4033}  
Iteration 2:{space 3}log likelihood = {res:-206.38084}  
Iteration 3:{space 3}log likelihood = {res:-206.38084}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       324
{txt}{col 49}LR chi2({res}12{txt}){col 67}= {res}     36.20
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0003
{txt}Log likelihood = {res}-206.38084{txt}{col 49}Pseudo R2{col 67}= {res}    0.0806

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     support{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t1 {c |}{col 14}{res}{space 2}-.4019525{col 26}{space 2} .4387822{col 37}{space 1}   -0.92{col 46}{space 3}0.360{col 54}{space 4} -1.26195{col 67}{space 3} .4580447
{txt}{space 10}t2 {c |}{col 14}{res}{space 2} .1916403{col 26}{space 2} .4746688{col 37}{space 1}    0.40{col 46}{space 3}0.686{col 54}{space 4}-.7386934{col 67}{space 3} 1.121974
{txt}{space 10}t3 {c |}{col 14}{res}{space 2} .1549391{col 26}{space 2} .4987552{col 37}{space 1}    0.31{col 46}{space 3}0.756{col 54}{space 4}-.8226032{col 67}{space 3} 1.132481
{txt}{space 10}t4 {c |}{col 14}{res}{space 2} .5995972{col 26}{space 2} .4784861{col 37}{space 1}    1.25{col 46}{space 3}0.210{col 54}{space 4}-.3382183{col 67}{space 3} 1.537413
{txt}{space 10}t5 {c |}{col 14}{res}{space 2}-.3982989{col 26}{space 2} .4830809{col 37}{space 1}   -0.82{col 46}{space 3}0.410{col 54}{space 4} -1.34512{col 67}{space 3} .5485223
{txt}{space 10}t6 {c |}{col 14}{res}{space 2}-.3621605{col 26}{space 2} .4492464{col 37}{space 1}   -0.81{col 46}{space 3}0.420{col 54}{space 4}-1.242667{col 67}{space 3} .5183462
{txt}{space 10}t7 {c |}{col 14}{res}{space 2}-.7405976{col 26}{space 2} .4503003{col 37}{space 1}   -1.64{col 46}{space 3}0.100{col 54}{space 4} -1.62317{col 67}{space 3} .1419748
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0467369{col 26}{space 2} .2466455{col 37}{space 1}    0.19{col 46}{space 3}0.850{col 54}{space 4}-.4366793{col 67}{space 3} .5301532
{txt}{space 5}married {c |}{col 14}{res}{space 2} .4461423{col 26}{space 2} .2468643{col 37}{space 1}    1.81{col 46}{space 3}0.071{col 54}{space 4}-.0377028{col 67}{space 3} .9299874
{txt}{space 2}employment {c |}{col 14}{res}{space 2}-.1599932{col 26}{space 2} .2373563{col 37}{space 1}   -0.67{col 46}{space 3}0.500{col 54}{space 4} -.625203{col 67}{space 3} .3052167
{txt}{space 4}democrat {c |}{col 14}{res}{space 2}-1.052764{col 26}{space 2} .2707887{col 37}{space 1}   -3.89{col 46}{space 3}0.000{col 54}{space 4}  -1.5835{col 67}{space 3}-.5220275
{txt}{space 4}nonwhite {c |}{col 14}{res}{space 2}-.0778871{col 26}{space 2}  .277744{col 37}{space 1}   -0.28{col 46}{space 3}0.779{col 54}{space 4}-.6222554{col 67}{space 3} .4664811
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2404094{col 26}{space 2} .4137294{col 37}{space 1}    0.58{col 46}{space 3}0.561{col 54}{space 4}-.5704853{col 67}{space 3} 1.051304
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit support t1 t2 t3 t4 t5 t6 t7 army female married employment democrat nonwhite if closemilitaryties==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-224.48091}  
Iteration 1:{space 3}log likelihood = {res:-203.75066}  
Iteration 2:{space 3}log likelihood = {res:-203.73661}  
Iteration 3:{space 3}log likelihood = {res:-203.73661}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       324
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}     41.49
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0001
{txt}Log likelihood = {res}-203.73661{txt}{col 49}Pseudo R2{col 67}= {res}    0.0924

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     support{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t1 {c |}{col 14}{res}{space 2}-.3712063{col 26}{space 2} .4412036{col 37}{space 1}   -0.84{col 46}{space 3}0.400{col 54}{space 4}-1.235949{col 67}{space 3}  .493537
{txt}{space 10}t2 {c |}{col 14}{res}{space 2} .2193639{col 26}{space 2} .4807618{col 37}{space 1}    0.46{col 46}{space 3}0.648{col 54}{space 4}-.7229119{col 67}{space 3}  1.16164
{txt}{space 10}t3 {c |}{col 14}{res}{space 2} .1321873{col 26}{space 2} .5029677{col 37}{space 1}    0.26{col 46}{space 3}0.793{col 54}{space 4}-.8536114{col 67}{space 3} 1.117986
{txt}{space 10}t4 {c |}{col 14}{res}{space 2} .5495883{col 26}{space 2}  .483355{col 37}{space 1}    1.14{col 46}{space 3}0.256{col 54}{space 4}-.3977701{col 67}{space 3} 1.496947
{txt}{space 10}t5 {c |}{col 14}{res}{space 2}-.4571411{col 26}{space 2} .4874413{col 37}{space 1}   -0.94{col 46}{space 3}0.348{col 54}{space 4}-1.412508{col 67}{space 3} .4982262
{txt}{space 10}t6 {c |}{col 14}{res}{space 2}-.3803916{col 26}{space 2}  .453656{col 37}{space 1}   -0.84{col 46}{space 3}0.402{col 54}{space 4}-1.269541{col 67}{space 3} .5087579
{txt}{space 10}t7 {c |}{col 14}{res}{space 2}-.7881582{col 26}{space 2} .4553477{col 37}{space 1}   -1.73{col 46}{space 3}0.083{col 54}{space 4}-1.680623{col 67}{space 3} .1043069
{txt}{space 8}army {c |}{col 14}{res}{space 2}-.5529216{col 26}{space 2} .2416453{col 37}{space 1}   -2.29{col 46}{space 3}0.022{col 54}{space 4}-1.026538{col 67}{space 3}-.0793055
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0355072{col 26}{space 2} .2487631{col 37}{space 1}    0.14{col 46}{space 3}0.886{col 54}{space 4}-.4520594{col 67}{space 3} .5230739
{txt}{space 5}married {c |}{col 14}{res}{space 2} .4619784{col 26}{space 2} .2495261{col 37}{space 1}    1.85{col 46}{space 3}0.064{col 54}{space 4}-.0270837{col 67}{space 3} .9510405
{txt}{space 2}employment {c |}{col 14}{res}{space 2}-.1572452{col 26}{space 2} .2394203{col 37}{space 1}   -0.66{col 46}{space 3}0.511{col 54}{space 4}-.6265003{col 67}{space 3} .3120099
{txt}{space 4}democrat {c |}{col 14}{res}{space 2}-1.041687{col 26}{space 2} .2728597{col 37}{space 1}   -3.82{col 46}{space 3}0.000{col 54}{space 4}-1.576483{col 67}{space 3} -.506892
{txt}{space 4}nonwhite {c |}{col 14}{res}{space 2}-.0402778{col 26}{space 2} .2810113{col 37}{space 1}   -0.14{col 46}{space 3}0.886{col 54}{space 4}-.5910498{col 67}{space 3} .5104943
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}   .54479{col 26}{space 2} .4387658{col 37}{space 1}    1.24{col 46}{space 3}0.214{col 54}{space 4}-.3151751{col 67}{space 3} 1.404755
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit support t1 t2 t3 t4 t5 t6 t7 army knowkilled knowwounded female married employment democrat nonwhite if closemilitaryties==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-224.48091}  
Iteration 1:{space 3}log likelihood = {res:-201.29478}  
Iteration 2:{space 3}log likelihood = {res:-201.28029}  
Iteration 3:{space 3}log likelihood = {res:-201.28029}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       324
{txt}{col 49}LR chi2({res}15{txt}){col 67}= {res}     46.40
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-201.28029{txt}{col 49}Pseudo R2{col 67}= {res}    0.1034

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     support{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t1 {c |}{col 14}{res}{space 2}-.3401792{col 26}{space 2} .4427662{col 37}{space 1}   -0.77{col 46}{space 3}0.442{col 54}{space 4}-1.207985{col 67}{space 3} .5276267
{txt}{space 10}t2 {c |}{col 14}{res}{space 2} .1815201{col 26}{space 2} .4862191{col 37}{space 1}    0.37{col 46}{space 3}0.709{col 54}{space 4}-.7714518{col 67}{space 3} 1.134492
{txt}{space 10}t3 {c |}{col 14}{res}{space 2} .0815706{col 26}{space 2} .5087691{col 37}{space 1}    0.16{col 46}{space 3}0.873{col 54}{space 4}-.9155986{col 67}{space 3}  1.07874
{txt}{space 10}t4 {c |}{col 14}{res}{space 2}   .47551{col 26}{space 2} .4868164{col 37}{space 1}    0.98{col 46}{space 3}0.329{col 54}{space 4}-.4786326{col 67}{space 3} 1.429653
{txt}{space 10}t5 {c |}{col 14}{res}{space 2}-.4702935{col 26}{space 2} .4920824{col 37}{space 1}   -0.96{col 46}{space 3}0.339{col 54}{space 4}-1.434757{col 67}{space 3} .4941702
{txt}{space 10}t6 {c |}{col 14}{res}{space 2}-.4014175{col 26}{space 2} .4558611{col 37}{space 1}   -0.88{col 46}{space 3}0.379{col 54}{space 4}-1.294889{col 67}{space 3} .4920539
{txt}{space 10}t7 {c |}{col 14}{res}{space 2} -.872241{col 26}{space 2} .4600552{col 37}{space 1}   -1.90{col 46}{space 3}0.058{col 54}{space 4}-1.773933{col 67}{space 3} .0294507
{txt}{space 8}army {c |}{col 14}{res}{space 2}-.6206215{col 26}{space 2} .2461752{col 37}{space 1}   -2.52{col 46}{space 3}0.012{col 54}{space 4}-1.103116{col 67}{space 3} -.138127
{txt}{space 2}knowkilled {c |}{col 14}{res}{space 2} .4001744{col 26}{space 2} .3446794{col 37}{space 1}    1.16{col 46}{space 3}0.246{col 54}{space 4}-.2753847{col 67}{space 3} 1.075734
{txt}{space 1}knowwounded {c |}{col 14}{res}{space 2} .1805767{col 26}{space 2} .3479085{col 37}{space 1}    0.52{col 46}{space 3}0.604{col 54}{space 4}-.5013114{col 67}{space 3} .8624648
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0137115{col 26}{space 2} .2509912{col 37}{space 1}    0.05{col 46}{space 3}0.956{col 54}{space 4}-.4782221{col 67}{space 3} .5056452
{txt}{space 5}married {c |}{col 14}{res}{space 2} .4510983{col 26}{space 2} .2515918{col 37}{space 1}    1.79{col 46}{space 3}0.073{col 54}{space 4}-.0420126{col 67}{space 3} .9442092
{txt}{space 2}employment {c |}{col 14}{res}{space 2}-.2284985{col 26}{space 2}  .244354{col 37}{space 1}   -0.94{col 46}{space 3}0.350{col 54}{space 4}-.7074235{col 67}{space 3} .2504265
{txt}{space 4}democrat {c |}{col 14}{res}{space 2}-1.011673{col 26}{space 2} .2743991{col 37}{space 1}   -3.69{col 46}{space 3}0.000{col 54}{space 4}-1.549485{col 67}{space 3}-.4738605
{txt}{space 4}nonwhite {c |}{col 14}{res}{space 2}-.0727201{col 26}{space 2} .2841273{col 37}{space 1}   -0.26{col 46}{space 3}0.798{col 54}{space 4}-.6295995{col 67}{space 3} .4841592
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3611612{col 26}{space 2} .4483406{col 37}{space 1}    0.81{col 46}{space 3}0.421{col 54}{space 4}-.5175703{col 67}{space 3} 1.239893
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *clarify, using model 1*
. logit support t1 t2 t3 t4 t5 t6 t7 closemilitaryties female married employment democrat nonwhite

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-814.19159}  
Iteration 1:{space 3}log likelihood = {res:-779.67635}  
Iteration 2:{space 3}log likelihood = {res:-779.52134}  
Iteration 3:{space 3}log likelihood = {res:-779.52132}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,201
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}     69.34
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-779.52132{txt}{col 49}Pseudo R2{col 67}= {res}    0.0426

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          support{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}t1 {c |}{col 19}{res}{space 2}-.1879343{col 31}{space 2} .2415808{col 42}{space 1}   -0.78{col 51}{space 3}0.437{col 59}{space 4}-.6614239{col 72}{space 3} .2855554
{txt}{space 15}t2 {c |}{col 19}{res}{space 2} .1099664{col 31}{space 2} .2397594{col 42}{space 1}    0.46{col 51}{space 3}0.646{col 59}{space 4}-.3599535{col 72}{space 3} .5798862
{txt}{space 15}t3 {c |}{col 19}{res}{space 2}-.0078664{col 31}{space 2} .2397693{col 42}{space 1}   -0.03{col 51}{space 3}0.974{col 59}{space 4}-.4778057{col 72}{space 3} .4620728
{txt}{space 15}t4 {c |}{col 19}{res}{space 2} .1572785{col 31}{space 2} .2394301{col 42}{space 1}    0.66{col 51}{space 3}0.511{col 59}{space 4} -.311996{col 72}{space 3} .6265529
{txt}{space 15}t5 {c |}{col 19}{res}{space 2}-.0509555{col 31}{space 2} .2413135{col 42}{space 1}   -0.21{col 51}{space 3}0.833{col 59}{space 4}-.5239213{col 72}{space 3} .4220103
{txt}{space 15}t6 {c |}{col 19}{res}{space 2} .2589451{col 31}{space 2} .2393605{col 42}{space 1}    1.08{col 51}{space 3}0.279{col 59}{space 4} -.210193{col 72}{space 3} .7280831
{txt}{space 15}t7 {c |}{col 19}{res}{space 2}-.1605163{col 31}{space 2} .2401565{col 42}{space 1}   -0.67{col 51}{space 3}0.504{col 59}{space 4}-.6312143{col 72}{space 3} .3101818
{txt}closemilitaryties {c |}{col 19}{res}{space 2} .3530771{col 31}{space 2} .1376643{col 42}{space 1}    2.56{col 51}{space 3}0.010{col 59}{space 4} .0832601{col 72}{space 3} .6228941
{txt}{space 11}female {c |}{col 19}{res}{space 2}-.0977449{col 31}{space 2} .1236762{col 42}{space 1}   -0.79{col 51}{space 3}0.429{col 59}{space 4}-.3401458{col 72}{space 3}  .144656
{txt}{space 10}married {c |}{col 19}{res}{space 2} .2182081{col 31}{space 2} .1228296{col 42}{space 1}    1.78{col 51}{space 3}0.076{col 59}{space 4}-.0225335{col 72}{space 3} .4589497
{txt}{space 7}employment {c |}{col 19}{res}{space 2} .0143482{col 31}{space 2} .1222334{col 42}{space 1}    0.12{col 51}{space 3}0.907{col 59}{space 4}-.2252249{col 72}{space 3} .2539213
{txt}{space 9}democrat {c |}{col 19}{res}{space 2}-.8624574{col 31}{space 2} .1330743{col 42}{space 1}   -6.48{col 51}{space 3}0.000{col 59}{space 4}-1.123278{col 72}{space 3}-.6016366
{txt}{space 9}nonwhite {c |}{col 19}{res}{space 2}-.0540356{col 31}{space 2} .1428799{col 42}{space 1}   -0.38{col 51}{space 3}0.705{col 59}{space 4} -.334075{col 72}{space 3} .2260038
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} -.221224{col 31}{space 2}  .215537{col 42}{space 1}   -1.03{col 51}{space 3}0.305{col 59}{space 4}-.6436686{col 72}{space 3} .2012207
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estsimp logit support t1 t2 t3 t4 t5 t6 t7 closemilitaryties female married employment democrat nonwhite

{txt}Iteration 0:   log likelihood = {res}-814.19159
{txt}Iteration 1:   log likelihood = {res}-779.67635
{txt}Iteration 2:   log likelihood = {res}-779.52134
{txt}Iteration 3:   log likelihood = {res}-779.52132

{txt}Logistic regression                               Number of obs   = {res}      1201
                                                  {txt}LR chi2({res}13{txt})     = {res}     69.34
                                                  {txt}Prob > chi2     = {res}    0.0000
{txt}Log likelihood = {res}-779.52132                       {txt}Pseudo R2       = {res}    0.0426

{txt}{hline 13}{c TT}{hline 64}
     support {c |}      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
{hline 13}{c +}{hline 64}
          t1 {c |}  {res}-.1879343   .2415808    -0.78   0.437    -.6614239    .2855553
          {txt}t2 {c |}  {res} .1099664   .2397594     0.46   0.646    -.3599534    .5798862
          {txt}t3 {c |}  {res}-.0078664   .2397693    -0.03   0.974    -.4778056    .4620728
          {txt}t4 {c |}  {res} .1572785   .2394301     0.66   0.511     -.311996     .626553
          {txt}t5 {c |}  {res}-.0509555   .2413135    -0.21   0.833    -.5239213    .4220103
          {txt}t6 {c |}  {res} .2589451   .2393605     1.08   0.279    -.2101929    .7280832
          {txt}t7 {c |}  {res}-.1605163   .2401565    -0.67   0.504    -.6312143    .3101818
{txt}closemilit~s {c |}  {res} .3530771   .1376643     2.56   0.010     .0832601    .6228941
      {txt}female {c |}  {res}-.0977449   .1236762    -0.79   0.429    -.3401458     .144656
     {txt}married {c |}  {res} .2182081   .1228296     1.78   0.076    -.0225335    .4589497
  {txt}employment {c |}  {res} .0143482   .1222334     0.12   0.907    -.2252249    .2539213
    {txt}democrat {c |}  {res}-.8624575   .1330743    -6.48   0.000    -1.123278   -.6016367
    {txt}nonwhite {c |}  {res}-.0540356   .1428799    -0.38   0.705     -.334075    .2260038
       {txt}_cons {c |}  {res} -.221224    .215537    -1.03   0.305    -.6436687    .2012207
{txt}{hline 13}{c BT}{hline 64}

{res}Simulating main parameters.  Please wait....
% of simulations completed: 7% 14% 21% 28% 35% 42% 50% 57% 64% 71% 78% 85% 92% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14
{txt}
{com}. 
. *next, for marital status*
. setx t1 0 t2 0 t3 0 t4 0 t5 0 t6 0 t7 0 closemilitaryties 0 female 1 married 0 employment 0 democrat 0 nonwhite 0
{txt}
{com}. simqi, pr

{txt}      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
             Pr(support=0) |  {res} .5780533     .0517257     .4638336    .6810674
             {txt}Pr(support=1) |  {res} .4219467     .0517257     .3189326    .5361664
{txt}
{com}. setx t1 0 t2 0 t3 0 t4 0 t5 0 t6 0 t7 0 closemilitaryties 0 female 1 married 1 employment 0 democrat 0 nonwhite 0
{txt}
{com}. simqi, pr

{txt}      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
             Pr(support=0) |  {res} .5245511     .0508045     .4254928    .6225809
             {txt}Pr(support=1) |  {res} .4754489     .0508045     .3774191    .5745072
{txt}
{com}. *next, for close military ties *
. setx t1 0 t2 0 t3 0 t4 0 t5 0 t6 0 t7 0 closemilitaryties 0 female 1 married 1 employment 0 democrat 0 nonwhite 0
{txt}
{com}. simqi, pr

{txt}      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
             Pr(support=0) |  {res} .5245511     .0508045     .4254928    .6225809
             {txt}Pr(support=1) |  {res} .4754489     .0508045     .3774191    .5745072
{txt}
{com}. setx t1 0 t2 0 t3 0 t4 0 t5 0 t6 0 t7 0 closemilitaryties 1 female 1 married 1 employment 1 democrat 0 nonwhite 0
{txt}
{com}. simqi, pr

{txt}      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
             Pr(support=0) |  {res}  .435192     .0573578     .3205477     .551483
             {txt}Pr(support=1) |  {res}  .564808     .0573578      .448517    .6794523
{txt}
{com}. *next, for democrats*
. setx t1 0 t2 0 t3 0 t4 0 t5 0 t6 0 t7 0 closemilitaryties 0 female 1 married 1 employment 0 democrat 0 nonwhite 0
{txt}
{com}. simqi, pr

{txt}      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
             Pr(support=0) |  {res} .5245511     .0508045     .4254928    .6225809
             {txt}Pr(support=1) |  {res} .4754489     .0508045     .3774191    .5745072
{txt}
{com}. setx t1 0 t2 0 t3 0 t4 0 t5 0 t6 0 t7 0 closemilitaryties 0 female 1 married 1 employment 0 democrat 1 nonwhite 0
{txt}
{com}. simqi, pr

{txt}      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
             Pr(support=0) |  {res}  .719939     .0457312     .6244317    .8045049
             {txt}Pr(support=1) |  {res}  .280061     .0457312     .1954951    .3755683
{txt}
{com}. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/nisha/Dropbox/Casualties of War/APSA 2015/Summer 2018/Sent to ISQ/Slightly revised version/Revisions for R&R/Resubmitted to ISQ/Corrected version for R&R/First postaccepted version/life&limbreplication.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 3 Aug 2020, 12:27:43
{txt}{.-}
{smcl}
{txt}{sf}{ul off}